Download “Best Practices for Ensuring Data Quality & Relevancy for Gen AI”.
Katya Linossi, Co-Founder and CEO
More blogs by this author
Katya Linossi, Co-Founder and CEO
More blogs by this authorIndustry experts widely acknowledge that if firms neglect to invest in knowledge management (KM), they face a significant risk of quickly falling behind. This is due to the critical role that knowledgeplays in providing value to Generative Artificial Intelligence (Gen AI) applications.
Many Gen AI tools use large datasets and often prioritise speed over accuracy. This blog highlights the importance of validated, contextual knowledge, especially in sectors like legal, finance, and healthcare, where data accuracy is critical.
As Gen AI shifts from automating tasks to supporting expert work, its value depends on how well firms capture, curate, and use their knowledge.
Postponing the investment can lead to significant consequences over time:
The inability to integrate connected intelligence prevents firms from providing Gen AI-enhanced experiences in both client and employee interactions.
At a recent legal conference, it was emphasized once again that AI and KM are integrally connected. Knowledge Management teams have evolved beyond managing precedent documents or intranet pages; they now spearhead Gen AI initiatives and strategies, unearth insights and enhance workflows. Indeed, the boundaries between Innovation, KM, and IT are increasingly blurred while collaboration across these functions is increasing.
Knowledge Management provides the critical foundation for AI by delivering essential context, structure, and understanding that AI systems require to operate effectively. KM ensures that information is well-organized, accurately tagged, and contextualized, which enables AI solutions to interpret and apply knowledge appropriately in complex scenarios. With the evolution of Gen AI, users can now leverage these systems to accelerate knowledge discovery, automate and refine content curation, and deliver highly tailored information in real-time.
This convergence between KM and Gen AI represents a fundamental shift in how organizations create and leverage knowledge. By integrating robust knowledge management practices with advanced AI, firms elevate the value of both disciplines. KM ensures that data feeding into Gen AI is relevant, reliable, and current, while Gen AI amplifies KM by surfacing hidden insights, enabling more intelligent workflows, and reducing manual effort. This dynamic creates a powerful cycle: KM empowers AI to be more accurate and relevant, and AI transforms traditional KM into a proactive, strategic driver of organizational intelligence. Together, this synergy unlocks new possibilities for productivity, sparks innovation, and delivers sustainable competitive advantage in rapidly evolving industries.
The effective management of knowledge creation, transfer, and utilization is increasingly vital for the survival and success of firms. According to Deloitte, while knowledge management (KM) ranked as one of the top three issues influencing business success, only 9% of companies felt ready to address it. This disconnect is now a risk, especially with AI adoption accelerating in knowledge-intensive sectors like legal, finance, and professional services.
When done right, AI-powered KM yields exponential returns:
Accenture echoes this urgency: only 12% of firms have reached an AI maturity level capable of delivering enterprise transformation. The gap lies not in ambition but in readiness and specifically, knowledge readiness.
As highlighted in recent news stories, such as lawyers referencing fabricated cases, Gen AI systems demonstrate the risks involved when operating without access to verified information. Gen AI is fundamentally dependent on a foundation of high-quality, well-structured, and contextually relevant knowledge to deliver trustworthy, actionable outcomes. This dependency spans all forms of AI, including large language models, knowledge graphs, and sophisticated enterprise automation tools.
When firms provide Gen AI with curated, authoritative knowledge, the technology is empowered to analyze, summarize, and generate insights that are not only accurate but also aligned with specific sector requirements. For example, even minor inaccuracies can carry significant consequences, making reliable data and robust knowledge frameworks non-negotiable.
Ultimately, the depth, quality, and accessibility of knowledge is how it is collected, contextualised, maintained, and surfaced. This directly correlates with Gen AI's impact, influencing its ability to support complex decision-making, automate routine processes, and deliver sustained business value.
Large Language Models (LLMs) are only as useful as the content they are exposed to. Their capabilities such as summarizing a document, drafting a proposal, or recommending next steps, stem from the quality, completeness, and contextual relevance of the content they process during a prompt-response interaction.
When LLMs operate without access to validated, structured, and contextual data, they don’t just produce incomplete outputs, they can fabricate facts, misinterpret queries, and present plausible sounding but entirely incorrect information. This phenomenon, widely referred to as "AI hallucination", is the technical consequence of information gaps.
In other words, if the model doesn’t know, it will guess. That might be acceptable for casual use but in high-stakes sectors like law, finance, or healthcare, these gaps are unacceptable risks.
Platforms such as Atlas, minimize the risk of hallucinations and delivers highly precise outcomes by orchestrating several operational parameters, including ensuring LLMs operate only on curated, contextual, and validated knowledge. It closes information gaps through enriched tagging and structured content collections.
Another example of orchestration within Atlas, is the built-in adjustable controls for strict grounding to enforce compliance and maintain confidence in Gen AI-generated outputs.
Knowledge is only useful when it’s contextual and Gen AI is only as smart as the context it can understand and apply.
There is no doubt that metadata is the holy grail of precise Gen AI responses. Metadata gives meaning to data and plays a crucial role in enhancing the performance, accuracy, and effectiveness of Gen AI systems. Gartner emphasizes that robust metadata management is crucial for both Gen AI readiness and the success of Gen AI initiatives.
Atlas enhances (as a unique capability) the effectiveness of LLMs by ingesting and incorporating the contextual metadata from relevant subsets of enterprise knowledge, which reduces, or eliminates, inaccuracies and improves alignment with reliable sources and end-user trust.
With regulations like the EU AI Act and ISO/IEC 42001 now in effect, AI governance is no longer optional. Firms must demonstrate data lineage, source traceability, and model accountability. A robust platform like Atlas, with its AI orchestration capabilities, provides this control layer and transparency for AI compliance.
Gen AI has enormous potential in the legal sector but only when paired with high-quality, contextual knowledge. While GenAI excels at improving speed and automating tasks, it doesn’t inherently enhance intelligence. In fact, without a trusted foundation of validated, well-structured content, it risks producing outputs that are inaccurate or misleading.
However, when grounded in authoritative legal knowledge, GenAI becomes far more than a productivity tool. It evolves into a reasoning knowledge assistant that can support nuanced decision-making, accelerate complex workflows, and enhance client service.
The firms that will gain the greatest competitive advantage are not those that simply deploy AI tools, but those that treat knowledge as a strategic asset.
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